Recommender engine and user model for transmedia content data

ABSTRACT

A recommender engine is configured to access memory and surface transmedia content items; and/or linked transmedia content subsets; and/or one or more identifications of identified users; and/or content items of the plurality of transmedia content items associated with at least one identified user. The surfaced items are presented for selection by the given user via the transmedia content linking engine as one or more user-selected transmedia content items.

FIELD OF DISCLOSURE

The present disclosure relates to an apparatus, system and method forprocessing transmedia content data. For example, the disclosure providesfor identifying and surfacing recommendations of transmedia content tousers for processing of transmedia content, and additionally thedisclosure also provides for user modelling of users accessingtransmedia content data.

BACKGROUND

Influenced by a variety of different multimedia content types, newdigital distribution channels, mobile communication devices and an everincreasing use of social media, industry is currently experiencing adisruption in how media is created, distributed and consumed. Classicalproduction pipelines have become less effective as audiences movetowards anytime, anywhere, personalized consumption, substitutingTV-centric models with multi-device, multichannel models. Individualcustomers and groups of customers have also become more interactive andparticipatory, contributing significantly to the creation of new media.The cycles in the traditional creation-distribution-consumption loopbecome much shorter as consumers constantly provide feedback, resultingin a trend towards ultrashort form content.

Existing delivery platforms, for example YouTube and Facebook, allow forthe creation and editing of simple channel based content, using a basicmodel whereby content creators can upload content such as video, text orimages, and users can consume the content in an isolated, linear andmono-medial manner. This can often be done in conjunction with mediapresented via other platforms such as television or print media.

At the same time, functionality provided by existing multimediaplatforms allows the sharing of user-generated content, which, alongwith social networking, is transforming the media ecosystem. Mobilephones, digital cameras and other pervasive devices produce huge amountsof data that users can continuously distribute in real time.Consequently, content sharing and distribution needs will continue toincrease. The content can be of many different forms, known collectivelyas “transmedia” content.

Existing systems that allow users to generate, organize and sharecontent are generally hard to control: these systems do not offeradequate tools for predicting what the next big trend will be, and whichgroupings of time-ordered content resonate with particular audiences.Furthermore, visualising the large amount of multimedia information in away which users can explore and consume is challenging. In particular,visualisation of such large data sets is challenging in terms ofperformance, especially on lower-power devices such as smartphones ortablets. It is desirable that any visualisation of the data could berendered in real time such that immediate visual feedback is provided toa user exploring the data.

The ever-growing availability of content to multiple users and theever-increasing power of computing resources available to individualusers is driving users towards their own individual creation of content,with such content being in multiple formats. This progression can beseen in FIG. 1. User 10 consumes single content items 15. Withincreasing computing resources, user 20 has developed into aninteractive user making choices which affect the flow of individualcontent items 25 to the user 20. Further, user 30 has recently becomemore common by generating multiple personalised, individual contentitems 35 which can be accessed over the Internet 50 by other users. Aproblem now exists with a current user 40 who can access a considerableamount of different types of content items 35 over the Internet 50 anddesires to utilise such content. It would be desirable for user 40 to beable to contribute to and generate new structured groups 46 of linkedcontent items 45.

In utilising the content items 35, the amount of which can beconsiderable, it can be problematic for the user 40 to be able toreadily identify relevant and appropriate content for placing into thenew structured groups 46. Such content can additionally be identified byconsidering the content generated from other users. However, it can beequally problematic to identify the users who might be similar to user40, and thereby themselves be generating relevant and appropriatecontent.

SUMMARY OF THE DISCLOSURE

In a first aspect of this disclosure, there is provided an apparatus forsurfacing transmedia content to a given user of a plurality of userscomprising:

-   -   a memory configured to store:        -   a plurality of transmedia content items, each content item            being associated with at least one of the plurality of            users; and        -   linking data which define time-ordered content links between            the plurality of transmedia content items, the plurality of            transmedia content items being arranged into linked            transmedia content subsets comprising different groups of            the transmedia content items and different content links            therebetween;    -   a transmedia content linking engine configured to receive user        input indicative of a time-ordering between at least two        user-selected transmedia content items and generate the content        link data for storage in the memory, thereby defining a linked        transmedia content subset including the at least two        user-selected transmedia content items; and    -   a recommender engine configured to access the memory and surface        to the given user: one or more content items of the plurality of        transmedia content items; and/or one or more of the linked        transmedia content subsets of the linked transmedia content        subsets; and/or one or more identifications of identified users        other than the given user; and/or the content items of the        plurality of transmedia content items associated with at least        one identified user other than the given user,    -   wherein the one or more surfaced content items and/or the one or        more surfaced linked transmedia content subsets are surfaced for        selection by the given user via the transmedia content linking        engine as one or more user-selectable transmedia content items.

The apparatus thus provides for transmedia content, whether individualitems, subset groups thereof, or owners associated thereto, to bereadily identified and surfaced so that a given user can utilise suchcontent within their apparatus, even if it is associated with otherusers, for example by linking it to other content in a time-orderedfashion.

The apparatus may be implemented as a computing device, for example aserver computing device.

The memory may further include or be configured to store user ownershipdata which associates each transmedia content item to a user of theplurality of users, and each transmedia content subset of the multiplecontent subsets with a user of the plurality of users.

The transmedia content linking engine may be configured to identify acurrent user-selected time-ordered location of the given user within agiven linked transmedia content subset for insertion of one of thetransmedia content items, and to generate corresponding content linkingdata therefor upon insertion by the given user of the one or morecontent items into the given linked transmedia content subset.

The recommender engine may be further configured to surface the one ormore transmedia content items and/or surface the one or more transmediacontent subsets based on the current user-selected time-orderedlocation.

The apparatus may further comprise a preference model, wherein thepreference model is configured to generate a user-specific rating (orscore) for a given user for a given transmedia content data item.

The preference model can be configured to:

remove global effects for one or more rated items that have beenpreviously rated by the given user to generate explicit ratings(user-assigned ratings) and storing the removed global effects;

provide the explicit ratings and metadata associated with the giventransmedia content data item to a plurality of recommender algorithms togenerate a plurality of predicted ratings for the given transmediacontent data item;

combine the plurality of predicted ratings to build an ensembleprediction of the predicted ratings for the given transmedia contentdata item;

add the stored global effects to the ensemble prediction to produce apredicted user rating for the given transmedia content data item; and

output the predicted user rating for the given transmedia content dataitem.

The user assigned ratings previously assigned by individual users may bestored as metadata alongside each content item, or linked contentsubset. The user assigned ratings may be extracted by a metadataextraction component and fed to the preference model.

The apparatus may further comprise a user-brand match componentconfigured to provide for a given user a prediction of a preference fora given branded content data item.

The apparatus may further comprise a branded content model configured toprovide for a given transmedia content item a prediction of thesuitability of a given branded content data item.

The recommender engine may be further configured to surface transmediacontent data items by querying the preference model, user-brand matchcomponent and brand model component by providing the preference model,user-brand match component and brand model with a transmedia contentparameter, user data for the given user and a given branded content dataitem, and to maximise the sum of the output for the preference model,user-brand match component and brand model over the transmedia contentparameter.

The recommender engine may be configured to surface the transmediacontent data item with the maximum output.

The plurality of transmedia content items may comprise items ofdifferent content types.

The transmedia content data items may relate to narrative elements ofthe transmedia content data items. The time-ordered content links candefine a narrative order of the transmedia content data items. Eachtime-ordered content link can define a directional link from a firsttransmedia content data item to a second transmedia content data item ofthe plurality of transmedia content data items.

The first transmedia content data item may have a plurality of outgoingtime-ordered content links. The second transmedia content data item mayhave a plurality of incoming time-ordered content links.

The memory can be further configured to store a plurality of subsetentry points for the plurality of transmedia content subsets. Eachsubset entry point may be a flag indicating a transmedia content dataitem that has at least one outgoing time-ordered link and no incomingtime-ordered links. Each linked transmedia content subset can define alinear path, wherein the linear path comprises a subset entry point, oneor more transmedia content data items and one or more time ordered linksbetween the subset entry point and the transmedia content data items.Two or more transmedia content subsets can share one or more subsetentry points, one or more transmedia content data items and/or one ormore time ordered content links.

The recommender engine can be further configured to surface one or moregroup's transmedia content subsets to the given user, the one or moresurfaced groups being surfaced for selection by the given user via thetransmedia content linking engine.

The apparatus may further comprise a user model, wherein the user modelcan be configured to provide predictions of the state and/or behaviourof a given user to the recommender engine.

The memory may be further configured to store a plurality of historicaluser interaction data items which define the historical interaction of agiven user with the apparatus for surfacing transmedia content to agiven user of a plurality of users, and wherein the user model may beconfigured to:

-   -   receive one or more historical user interaction data items        associated with the given user;    -   provide the one or more historical interaction data items to a        statistical model to produce a prediction of a mental state of        the user;    -   retrieve from the statistical model the predicted mental state        of the user and/or a predicted behaviour of the user; and    -   output the predicted mental state of the user and/or predicted        behaviour of the user.

The statistical model may be a hidden Markov model.

The transmedia content recommender engine may be further configured tosurface the one or more individual content items to the given userand/or surface one or more of the linked transmedia content subsets tothe given user when the output of the user model indicates that the useris in a mental state conducive to consuming transmedia content dataitems.

The transmedia content recommender engine can be further configured tosurface the one or more individual content items to the given userand/or surface one or more of the linked transmedia content subsets tothe given user when the output of the user model indicates the predicteduser behaviour is to consume transmedia content data items.

In a second aspect of the present disclosure, there is provided a systemfor surfacing transmedia content to a given user of a plurality of userscomprising:

the aforementioned apparatus;

an electronic device configured to be in communication with theapparatus and receive and display to the given user: the surfaced one ormore content items of the plurality of transmedia content items; and/orthe surfaced one or more linked transmedia content subsets of the linkedtransmedia content subsets; and/or the surfaced one or moreidentifications of identified users other than the given user; and/orthe surfaced content items of the plurality of transmedia content itemsassociated with at least one identified user other than the given user.

In a third aspect of the present invention, there is provided A methodfor surfacing transmedia content to a given user of a plurality of usersfrom a memory configured to store a plurality of transmedia contentitems, each content item being associated with at least one of theplurality of users, and linking data which define time-ordered contentlinks between the plurality of transmedia content items, the pluralityof transmedia content items being arranged into linked transmediacontent subsets comprising different groups of the transmedia contentitems and different content links therebetween, the method comprising:

-   -   receiving, at a transmedia content linking engine, user input        indicative of a time-ordering between at least two user-selected        transmedia content items;    -   generating with the transmedia content linking engine the        content linking data for storage in the memory, thereby defining        a linked transmedia content subset including the at least two        user-selected transmedia content items; and    -   accessing the memory and surfacing to the given user with a        recommender engine: one or more content items of the plurality        of transmedia content items; and/or one or more of the linked        transmedia content subsets of the linked transmedia content        subsets; and/or one or more identifications of identified users        other than the given user; and/or the content items of the        plurality of transmedia content items associated with at least        one identified user other than the given user,    -   wherein the one or more surfaced content items and/or the one or        more surfaced linked transmedia content subsets are surfaced for        selection by the given user via the transmedia content linking        engine as one or more user-selectable transmedia content items.

The electronic device may be implemented as user equipment, which may bea computing device, for example a client computing device, such aspersonal computer, laptop or a handheld computing device. For example,the handheld computing device may be a mobile phone or smartphone. Theelectronic device may include a user input interface configured toprovide user input to the processing circuitry, such as for example theinstructions to create a new time-ordered content link. This may be oneor more of a keypad or touch-sensitive display. The electronic devicemay include a display (which may include the touch-sensitive display)configured to output data to a user interacting with the electronicdevice, including one or more of the content data items.

In a fourth aspect of the present invention, there is provided acomputer readable medium comprising computer executable instructions,which when executed by a computer, cause the computer to perform thesteps of the aforementioned method.

BRIEF DESCRIPTION OF DRAWINGS

The present invention is described in exemplary embodiments below withreference to the accompanying drawings in which:

FIG. 1 depicts how users interact with content items according to thepresent disclosure.

FIGS. 2A, 2B and 2C depict a linear transmedia content subset, groupednon-linear transmedia content subsets, and a subset universerespectively, each formed of multiple transmedia content data items andtime-ordered content links.

FIG. 3 depicts the architecture of the system of the present disclosure.

FIG. 4 depicts an exemplary apparatus on which the backend of thepresent disclosure operates.

FIG. 5 depicts an exemplary apparatus on which the frontend of thepresent disclosure operates.

FIG. 6 depicts exemplary non-linear networks of transmedia content dataitems according to the present disclosure.

FIG. 7 depicts a recommender engine of the apparatus of the presentdisclosure.

FIG. 8 depicts an exemplary preference model component of therecommender engine component according to the present disclosure.

FIG. 9 depicts a user model component of the apparatus of the presentdisclosure.

FIGS. 10A and 10B depict exemplary user interfaces that are rendered andoutput by the system.

FIG. 11 depicts how user state and actions can be modelled according tothe system.

FIG. 12 depicts a method for surfacing transmedia content to a useraccording to the present disclosure.

DETAILED DISCLOSURE

The present disclosure describes a new apparatus, system and method formanaging transmedia content. In one embodiment, there is disclosed aplatform for the creation, distribution and consumption of transmediacontent. The content may be arranged in a time-ordered manner forconsumption, thereby defining so-called “story” based content.

In the context of the present disclosure, groups of time-orderedcontent, for example in the form of stories, are made up of multipleelements of transmedia content, each being referred to herein as atransmedia content data items. Each item pertains to a narrative elementof the story. Each transmedia content data item may be linked, and thusconnected, to one or more other transmedia content data items in anordered fashion such that a user can navigate through subsets of thetransmedia content data items (also referred to as transmedia contentsubsets) in a time-ordered fashion to consume some or all of an entirestory.

The term “transmedia” means that the grouped content data items (whichare linked within the content subsets) comprise a plurality of differentmultimedia types, e.g. at least two different types of multimediacontent. For example, the different types of transmedia content data ofeach content data item within the subset can comprise at least twodifferent types from one or more of the following: textual data, imagedata, video data, audio data, animation data, graphical visualization orUI data, hypertext data, gaming data, interactive experience data,virtual reality (VR) data, augmented reality data, and multisensoryexperience data. Each transmedia content data item may itself comprisemultiple media types, e.g. video and audio data may be present within asingle item such that the audio is time-associated associated with thevideo.

The transmedia content data items can be grouped into transmedia contentsubsets. Each subset may be grouped based on one or more non-linearnetwork of the content data items.

Within each transmedia content subset, transmedia content data items arelinked to one another, directly or indirectly, by time-ordered linksbetween each data item. Typically, each time ordered-content link linkstwo transmedia content data items. An exception exists for a timeordered link which connects a subset entry point and a transmediacontent data item as explained below. The time-ordered link also definesa direction between the two transmedia content data items. The directionindicates an order in which linked transmedia content data items shouldbe presented to a user of the system. For example, when a firsttransmedia content data item is presented to a user, at least oneoutgoing time-ordered link (i.e. the direction defined by the link isaway from the first transmedia content data item) indicates a secondtransmedia content item that should be presented to the user next.

The term “subset” is used to denote a subset of transmedia content dataitems within the set of all transmedia content data items stored by thesystem. The transmedia content data items that are part of a transmediacontent subset are all directly or indirectly connected to each viatime-ordered content links between the transmedia content data items. Atransmedia content subset may be a linear, i.e. each transmedia contentdata item in the subset has at most one incoming time-ordered contentlink and one outgoing time-ordered content link, or a non-linearnetwork, i.e. one or more of the constituent transmedia content dataitems has more than one incoming or outgoing time-ordered content link.It will also be appreciated a non-linear network of transmedia contentdata items can be considered to be made up of multiple overlappinglinear paths from start-point to end-point through that network, andthat each linear path may also be considered to be a transmedia contentsubset. Furthermore, the term “group” has been used to denote acollection of transmedia content data items that are not necessarilyconnected, directly or indirectly, via time-ordered content links.However, where the term “group” has been used, a “subset” of transmediacontent data items, as defined above, may additionally be formed andutilised.

Each time-ordered link is stored in memory as link data comprising: theinput content data item; and the output content data item and thusimplicitly a direction between two content data items. Directional datafor the links is also stored which defines the path the links should beprocessed, and thus the order for processing of transmedia contentitems. The order can also be dependent on user interaction with eachitem as it is surfaced to a user.

Examples of transmedia content subsets are depicted in FIGS. 2A, 2B and2C. As mentioned above, the transmedia content data items are groupedinto transmedia content subsets.

In FIG. 2A, subset 100 defines a linear path of transmedia content dataitems 104 a-c. The subset 100 also comprises a subset entry point 102,which defines a starting point in the subset from which the system cancommence presenting the transmedia content data items within the subset.The subset entry point 102 may be linked to the first transmedia contentdata item 104 a by a time-ordered link, or may be a flag associated withthe first transmedia content data item 104 a which indicates that thetransmedia content data item 104 a is the first in the subset.

In the context of the present disclosure, the term “linear” means thateach transmedia content data item has, at most, only one incomingtime-ordered line (i.e. the direction defined by the link is inwardstowards the transmedia content data item) and only one outgoingtime-ordered link. The path defined by the transmedia content subset 100is unidirectional and there is only one possible route from the subsetentry point 102 and the final transmedia content data item of the path(i.e. a transmedia content data item with an incoming time-ordered linkbut no outgoing time ordered link).

The transmedia content data items may also be grouped based on multiplecontent subsets in a non-linear network, such as non-linear network 110depicted in FIG. 2B. In this context, the term “non-linear” means thattime-ordered links between the data items of each network may form aplurality of different paths through the network 110 which start indifferent places, end in different places, branch, split, diverge, leavesome data items out of the path and/or overlap with other paths. Such anon-linear network 110 can also be considered to be a group oftransmedia content subsets which share one or more transmedia contentdata items and/or time-ordered content links.

In the depicted non-linear network 110, each transmedia content dataitem 104 a-c, 112 a-b can have one or more incoming time-ordered linksand one or more outgoing time-ordered links. The data items 112 a, 104 band 112 b form a second transmedia content subset which shares the dataitem 104 b with the first transmedia content subset 100.

FIG. 2C depicts a story universe 120, in which multiple, relatednon-linear networks are grouped or clustered together. In oneembodiment, the non-linear networks of a story universe do not sharetransmedia content data items and/or time-ordered links with thenon-linear networks of another, different story universe. However, in analternative embodiment, the non-linear networks of a story universe doshare one or more transmedia content data items and/or time-orderedlinks with the non-linear networks of another, different story universe.

The system of the present disclosure manages the transmedia content dataitems, transmedia content subsets and one or more non-linear networks,facilitates the generation and manipulation of items between and withinsubsets and networks so that storylines can be formed. Accordingly, thecreation of transmedia content subsets and non-linear networks by a userof the system enables collaboration between users of the system andallows consumption of the created storylines. The architecture of thesystem is depicted in FIG. 3.

FIG. 3 depicts the overall architecture of the system 200. The system200 includes a frontend device 210, which is typically located on a userdevice such as a smartphone, tablet or PC that is operated directly bythe user of the system 200, and a backend device 230, which is typicallylocated on one or more servers that are connected to the user device viaa network such as the Internet.

The backend 230 contains global resources and processes that aremanaged, stored and executed at a central location or severaldistributed locations. The frontend 210 contains resources and processesthat are stored and executed on an individual user device. The backend230 is responsible for tasks that operate on large amounts of data andacross multiple users and stories, while the frontend 210 only hasaccess to the resources of a particular user (or a group of users) andfocuses on presentation and interaction.

The frontend 210 communicates with the backend 230 via the network, thecommunication layer 212 that is part of the frontend 210 and theexperience control layer 232 that is part of the backend 230. Theexperience control layer 232 is responsible for handling thedistribution of transmedia content data items, access limitations,security and privacy aspects, handling of inappropriate content dataitems, and user-specific limitations such as age group restrictions. Itensures that inappropriate, illegal, unlicensed or IP-violating contentis flagged and/or removed, either automatically, semi-automatically ormanually. It also handles sessions as the user interacts with the systemand provides session specific contextual information, including theuser's geolocation, consumption environment and consumption device,which can then be used by the frontend 210 to adapt the consumptionexperience accordingly. The experience control layer 232 also acts as acheckpoint for content validation, story verification, and story logic,in order to provide users with a consistent story experience.

The communication layer 212 performs client-side checks on permissions,content validation, and session management. While the final checkshappen at the experience control layer 232 of the backend 230, theadditional checks carried out in the communication layer 212 help inproviding a consistent experience to the user (e.g. not displayingcontent or features that cannot be accessed).

The frontend 210 also includes the user interface (UI) component 220,which is responsible for displaying and presenting the transmediacontent data items to users, including visual, auditory and textualrepresentations, and is also responsible for receiving the user's inputthrough pointing devices, touch events, text input devices, audiocommands, live video, or any other kind of interaction. The UI component218 can adapt to the user's location, environment, or current user statein order to provide an optimized experience.

The visual navigation component 214 is also included in the frontend210, and allows a user to explore, browse, filter and search thetransmedia content data items, transmedia content subsets and non-linearnetworks, and any other content provided by the platform. For navigationin the context of transmedia content and stories, the visual navigationcomponent 214 provides intelligent abstractions and higher-levelclusterings of transmedia content data items, transmedia content subsetsand non-linear networks, providing the user with an interface forinteractive visual exploration of the transmedia content, which enablesthe user to make navigation choices at a higher-level abstraction beforeexploring lower levels, down to single stories, i.e. transmedia contentsubsets, and individual transmedia content data items. The structure oftransmedia content subsets and non-linear network and the time-orderedlinks between transmedia content data items data items is visualized aswell, providing the user with information on how these data items arerelated to each other. In one embodiment of this visualisation, a graphstructure is employed, with nodes representing transmedia content dataitems, and connections representing the time-ordered content links. Inthe main, the evolution of the transmedia content subsets and non-linearnetworks is rendered in real-time as the subsets and non-linear networksare created and modified by all users of the system. In addition, in aparticular embodiment which is user initiated, for example via userselection or automatically based on user interaction, e.g. immediatelyor shortly after a given user logs in to the system, the recent pastevolution of the transmedia content subsets and non-linear networks,e.g. the evolution since last login of the given user can be displayedgraphically, e.g. in a time lapse rendering of the changes of thetransmedia content subsets and non-linear networks in the order in whichthey occurred.

The social interaction component 216 handles visualisations and userinput related to interactions between individual users of the system200. It provides tools for editing a user's public information, enablingnotifications on other users creating content (following), endorsing andrating other users' content, and directly interacting with other usersthrough real-time messaging systems, discussion boards, and videoconferencing. These tools also allow users to collaboratively create newcontent (i.e. transmedia content data items) and author and edit stories(i.e. transmedia content subsets and/or non-linear networks), as well asdefine the access rights and permissions associated with suchcollaborative work. The enforcement of these rights is handled by theexperience control layer 232, as mentioned above.

In addition to the experience control layer 232, the backend 230comprises a user model component 234, a story model component 236, arecommender engine 238, and a metadata extraction component 240, inaddition to one or more data stores or computer memory for storing datarelated to the transmedia content such as the transmedia content dataitems, linking data, transmedia content subsets, non-linear networks,and metadata relating to the individual data items and linking data aswell as to the subsets and non-linear networks.

The user model component 234 represents user behaviour and properties.It is driven by a suite of algorithms and data collections, includingbut not limited to statistics, analytics and machine learning algorithmsoperating on user interaction patterns, consumption behaviour and socialinteractions. The analytics happens in real-time and the user modelcomponent 234 is continuously updated as the user interacts with thesystem 200. Additional information such as the user's geolocation can betaken into account as well. The corresponding data is stored in a userdatabase. The user model component 234 also comprises models for groupsof several users, which for example emerge during multiusercollaboration, consumption, geolocation, or social interactions. As partof the user model component 234, users and/or groups of users areprofiled and characterized according to their personality, productivitystage and other criteria. The user model component allows the system tomake predictions of user behaviour under real or hypotheticalconditions, which then feed into the recommender engine component 238.The methods employed by the user model component for prediction userbehaviour and creative state are described below in more detail withrespect to FIG. 9. The user model component 234 also permits thecorrelation of interaction patterns of users not identified to thesystem so as to re-identify users probabilistically.

The user model component 234 is also connected to the talent harvestcomponent, which, based on user behaviour, identifies individual usersor groups of users that fulfil certain criteria such as, for example,having a large amount of users consuming or endorsing their work, havingsignificant influence on other users' behaviours and opinions, or beinghighly popular personalities. The talent harvest component, in concertwith the recommender engine component 238, then influences the behaviourof such users of the system 200.

The story model component 236 characterises the content of singletransmedia content data items, transmedia content subsets, non-linearnetworks and whole story universe, and stores the corresponding data ina story world database. The characterisations are found throughalgorithms such as, but not limited to, metadata extraction, analytics,graph analysis, or any other algorithms operating on connections betweencontent in general. Metadata extraction extends to include visual,auditory or textual elements, as well as higher-level concepts likecharacters, character personality traits, actions, settings, andenvironments. The characterisation also takes into account how usersinteract with the content, including the analysis of consumptionpatterns, content ratings and content-related social interactionbehaviour. The corresponding updates of the story model component 236happen in real-time as users interact with the system 200 or as newcontent is created or existing content is modified. Additionally, thestory model component 236 makes use of story, characterisations(including metadata) to model story logic. Using story reasoning, theconsistency of individual stories can be verified and logicalinconsistencies can be prevented either when a story is created or atthe time of consumption. The story model component 236 alsocommunication with the story harvest component, which uses the dataprovided by the story model component 236 in order to identify andextract content (transmedia media content data items, transmedia contentsubsets, non-linear networks or higher-level abstractions).

The recommender engine component 238 is in communication with both thestory model component 236 and the user model component 234 and providesconceptual connections between the story model component 236 and theuser model component 234. The recommender engine component 238 uses dataanalytics to match content (transmedia content data items, transmediacontent subsets, non-linear networks) with users, suggesting users forcollaboration, suggesting content to be consumed, or suggesting stories,story arcs or story systems to be extended with new content.Recommendations can be explicit, with recommendations being explicitlylabelled as such to the user, guiding the user through a transmediacontent subset or non-linear by providing an optimal consumption path orsuggesting other users to collaborate with, or they can be implicit,meaning the user's choice is biased towards certain elements of content(including transmedia content, advertisement, users), without making thebias explicitly visible to the user.

The metadata extraction component extracts metadata from transmediacontent (i.e. transmedia content data items, transmedia content subsetsand/or non-linear networks) automatically, semi-automatically, ormanually. The metadata extraction component 240 tags and annotatestransmedia content, providing a semantic abstraction not only of thecontent of individual transmedia content data items, but also of thetime-ordered links, transmedia content subsets, non-linear networks, andstory systems. The derived metadata thus spans a horizontal dimension(cross-domain, covering different types of media) as well as a verticalone (from single transmedia content data items to whole story systems).

Also depicted in FIG. 3 are story world database 280, media (content)database 282 and user database 284. The databases 280-284 are stored inmemory 301 of server device 230. The story world database 280 storesdata characterising and defining the structure of the transmedia contentsubsets, non-linear networks and whole story systems, for example by wayof linking data defining the subset structure. Additionally, metadataand graph analytics pertaining to the subsets and networks may also bestored in the story world database 280. The media database 282 storesindividual content items, and data characterising the content ofindividual transmedia content data items, e.g. metadata and graphanalytics for the individual content items. The user database 284 storesuser data pertaining to users of the system 200, including userbehaviour data defining how users have interacted with individualcontent items and subsets, and user preference data defining userindicated or derived preferences for content items and subsets.

FIG. 4 depicts an exemplary backend server device 230 on which thebackend of the system 200 is implemented. It will be appreciated thatthe backend 230 or functional components thereof may be implementedacross several servers or other devices. The server device 230 includesthe memory 301, processing circuitry 302 and a network interface 303.The memory may be any combination of one or more databases, otherlong-term storage such as a hard disk drive or solid state drive, orRAM. As described above, the memory 301 stores the transmedia contentdata items and associated linking data, which define time-orderedcontent links between the plurality of transmedia content data items.The plurality of transmedia content data items are arranged into linkedtransmedia content subsets comprising different groups of the transmediacontent data items and different content links therebetween. Theprocessing circuitry 302 is in communication with the memory 301 and isconfigured to receive instructions from a user device via the networkinterface to create new time-ordered content links between at least twoof the plurality of transmedia content data items and modify 301 thelinking data stored in the memory to include the new time-orderedcontent link.

FIG. 5 depicts an exemplary frontend electronic user device 210 on whichthe frontend of system 200 is provisioned. The user device 210 includesa memory 401, processing circuitry 402, network interface 403 and a userinterface 404. The user interface 404 may comprise one or more of: atouch-sensitive input, such as a touch-sensitive display, a touchscreen,pointing device, keyboard, display device, audio output device, and atablet/stylus. The network interface 403 may be in wired or wirelesscommunication with a network such as the Internet and, ultimately, theserver device 230 depicted in FIG. 4. The electronic device 210 receivesuser input at the user interface 404 and thereby communicates with theserver device 230 via the network interface 403 and network interface303, which provides the processor 302 with instructions to create newtime-ordered content links between the transmedia content data items inthe memory 301. The electronic device 210 may also provide instructionsto the server device 230 to delete or modify existing time-orderedcontent links and/or transmedia content data items from the memory.

It will be appreciated that the system may comprise multiple frontendelectronic devices 210, each configured to receive user input andthereby communicate with the server device 230 and provide instructionsto create, delete or modify time-ordered content links between thetransmedia content data items. Thus, multiple electronic devices 210,each being accessed by a different user, are adapted to process commoncontent links and content data items which are accessible to multipleusers.

The memory 301 of the server device 230 may also store user data items,which are associated with users of the system 200 and comprise useridentification data, such as a username, password, email address,telephone number and other profile information. The user data items mayalso comprise, for each user of the system user preference datapertaining to each user's preferences, user behaviour data pertaining tothe each user's online behaviours, user interaction data pertaining tothe each user's interaction with other users, and/or user location datapertaining to the current determined and/or past determined location ofthe each user.

The server device 230 may also be configured to implement the user model234 of the system 200 as mentioned above as described below in furtherdetail with respect to FIG. 9. The processing circuitry 302 of thedevice 230 can use the user model 234 to identify user interactions ofthe users of the system 200 with the transmedia content data items andsubsequently update the user interaction data stored in the memory 301in accordance with the user interaction.

The memory 301 may also store content characterisation data items, whichcharacterise one or more of the transmedia content data items. Inparticular, the memory 301 may store auditory characterisation datawhich characterises auditory components the transmedia content dataitems, visual characterisation data which characterises visualcomponents of the transmedia content data items, textualcharacterisation data which characterises textual components of thetransmedia content data items and/or interaction characterisation datawhich characterises interactive components, such as games, or quizzes,or puzzles, of the one or more transmedia content data items. Theprocessing circuitry 303 of the server device 230 can be furtherconfigured to provide a content subset modelling engine that processesthe content characterisation data items for each transmedia content dataitem in a given transmedia content subset and generates unique subsetcharacterisation data for the transmedia content subset based on theprocessed characterisation data. The content subset modelling engine maybe provided by the story model component 236 mentioned above.

The processing circuitry 302 may also implement the transmedia contentrecommender engine 238, mentioned above and described below in moredetail with respect to FIG. 7. The recommender engine 238 is configuredto process the characterisation data items and the user data items for agiven user and identify transmedia content data items and surfaceidentification(s) of one or more of the transmedia content data itemsthat are predicted to be matched to users, and additionally can surfaceidentification(s) of other matched users of the system 200.

The processing circuitry 302 of the server device is also configured toimplement the experience control layer 232 mentioned above. Theexperience control layer 232 implements a permission control systemwhich is used to determine whether a given user has permission to view,edit, modify or delete a given transmedia content data item,time-ordered content like, transmedia content subset or non-linearnetwork. Collaboration is a challenge in itself; however, authorshipattribution and consistency in particular are supported. A balance isthen provided between a very rigid and tight permission system, whichmight hinder collaboration and discourage potential contributors fromsharing their ideas, and an open system which allows any user to modifyor delete the content contributed by other users.

For a given transmedia content subset or non-linear network, created byan original first user (referenced hereinafter as “Alice”), and whichconsists of individual transmedia content data items connected bytime-ordered content links therebetween, this transmedia content subsetor non-linear network is attributed to and owned by Alice in metadataassociated with the transmedia content data items, linking data and theoriginal transmedia content subset and/or non-linear networkexclusively. The experience control layer 232 only allows modificationsto the original transmedia content subset or non-linear network byAlice. The system 200 is also configured such that a systemadministrator user or moderator user can provide or change permissionsfor and between all individual users, regardless of permissions assignedby individual users.

A second user (referenced hereinafter as “Bob”) may wish to contributeto the transmedia content subset or non-linear network. Bob may wish toinsert a new transmedia content data item into the transmedia contentsubset or non-linear network, and/or Bob may wish to create new linkingdata that defines an alternative path through the non-linear network ortransmedia content subset.

The experience control layer 232 does not permit Bob to modify theoriginal transmedia content subset or non-linear network that areattributed to and owned by Alice in the metadata. Instead, theexperience control layer 232 instructs the processor 302 to create acopy of the original transmedia content subset or non-linear network inthe memory 301, which includes the changes to the transmedia contentdata items and/or linking data input by Bob.

The copy will be exclusively owned by Bob in the metadata associatedwith the copied transmedia content subset or non-linear network, and assuch the experience control layer 232 will permit only Bob to edit ormodify the copied transmedia content subset or non-linear network.However, the original transmedia content data items and linking datacontributed by Alice remain attributed to Alice in the metadata, andonly the new transmedia content data items and linking data areattributed to Bob in the metadata. The copied transmedia content subsetor non-linear network maintains a reference to the original transmediacontent subset or non-linear network.

As Bob interacts with the content by creating, modifying and editingcontent items, subsets and non-linear networks, it updates in real timesuch that all other users can see the changes as they happen, includingAlice.

In an alternative embodiment, Bob can interact with the content bycreating, modifying and editing content items, subsets and non-linearnetworks so that the changes at this stage can only be seen by Bob. WhenBob is finished and no longer wishes to modify the content, subsets ornon-linear networks, he can formally submit his changes and theexperience control layer 232 provides the copied transmedia contentsubset or non-linear network to the user, more particularly to the userdevice of the user that is indicated by the metadata of the originaltransmedia content subset or non-linear network as the owner, e.g.Alice's user device, for review. Alice may then choose to “merge” thecopied transmedia content subset or non-linear network with theoriginal. The experience control layer 232 will delete the original andmodify the metadata of the copy to indicate that Alice is owner of thecopied transmedia content subset or non-linear network, since Bob willhave previously been designated in the metadata as owner of any items,subsets or networks which were created or modified by him. The metadataof the individual transmedia content items and linking data, other thanthe owner, is left unchanged.

Alice may approve of the modifications made by Bob, but may wish keepthe modifications as an optional alternative. In this case, she willchoose to “branch” the original transmedia content subset or non-linearnetwork. The experience control layer 232 will modify the originaltransmedia content subset or non-linear network to include any newtransmedia content data items and linking data contributed by Bob. Themetadata of the individual transmedia content items and linking data isunchanged, and the metadata for the modified original transmedia contentsubset or non-linear network still identifies Alice as the owner.

Finally, Alice may disapprove of the modification made by Bob, and canchoose to “push out” Bob's version. This causes the experience controllayer 232 to remove the reference link from the copy to the original.Again, the metadata of the individual transmedia content items andlinking data is unchanged. This means that Bob's version of the contentitems, subset and non-linear networks as a result of his creation and/ormodifications are now distinct from Alice's, and exists separately inmemory with corresponding metadata to indicate that he is the owner ofthis version.

The system 200 and experience control layer 232 may allow Alice to fullydelete Bob's edit, or force the modification to be hidden from otherusers of the system. Allowing for this option might be required in somecases, for example when copyright is infringed or the contentcontributed by Bob is inappropriate.

As mentioned above, the system 200 structures groups of content subsets(“storyworlds”), i.e. non-linear networks, as directed graphs ofconnected transmedia story data items and storylines, i.e. transmediacontent subsets, as sequences of connected transmedia content data itemsout of the generated non-linear networks graphs.

The story model component 236 of the system 200 arranges the stories andstoryworlds at the transmedia content level based on complex graphmetrics. The transmedia content data items are nodes. Edges of the graphdefine the time-ordered content links between transmedia content dataitems. The edges and nodes of the graph may be assigned with weightsderived from story attributes, e.g. number of likes received by usersconsuming the story. The graph-based model defines all of the possibleconsumption flows throughout a given graph and allows identification oftransmedia content data items which play key roles within thestoryworld. FIG. 6 depicts a graph-model of two non-linear networks oftransmedia content data items.

Each depicted non-linear network, 510 and 520 includes at least twosubset entry points 511 and 521 which define starting points in thesubset (and also any non-linear networks that the subsets are part of)from which the system should begin presenting the transmedia contentdata items. Non-linear network 511 has two distinct end points 513, 516,which are transmedia content data items that have only incomingtime-ordered content links. End point 513 is preceded by a branch 512 ofthe non-linear network, which shares only its first data item in commonwith a second brand 514. Branch 514 has an alternate path 515 whichskips several of the transmedia content data items of the rest of thebranch and then re-joins the branch 515 to terminate at the end point516. In contrast, non-linear network 520 has four branches with fourdistinct end points 523, 525, 526 and 527, which share varying numbersof transmedia content data items in common with one another. The datathus generated and stored which is representative of the non-linearnetwork is structural data indicative of the nodes and edges and linkstherebetween, thereby defining the time-ordered structure of contentdata items in for the non-linear network.

In a hierarchical system containing a lot of changing content atdifferent levels (story systems, non-linear networks, transmedia contentsubsets and individual transmedia content data items) users can easilyget lost in irrelevant content (for the user) or unappealing content(for the user). An engine guiding the user and fostering the creation ofhigh quality content is thus provided.

As mentioned above, the system 200 further includes a recommender enginecomponent 238 in the backend device 230, which principally provides oneor both of two functions namely: (i) surface relevant transmedia contentdata items, whether as individual items, or as linked content subsets toa given users of the system; and (ii) surface relevant users and theircontent to the given user. References herein to the “given” user mean auser who is logged into the system 200 via their frontend device 210 andis accessing data, including content items and user data from thebackend device 230.

FIG. 7 shows data flows between some components of the backend device230, in particular, the inputs and outputs of the recommender enginecomponent 234 implemented in the backend device 230.

As mentioned above, the recommender engine component 238 permits usersvia the frontend device 210 to receive suggestions about possible storyelements, i.e. transmedia content data items, to be consumed and/orextended with new content by the given user. Due to the hierarchicnature of the system, recommendations can be issued at one or moredifferent levels of granularity, e.g. story system, non-linear networks,transmedia content subsets and individual transmedia content data items.Furthermore, recommendations are dynamic, i.e. they change withcontinuously evolving content. Recommendations also take into accountpreferences of the given user to keep the user engaged with processingthe time-arranged content items. This can mean that individual contentitems from the same or other users with sufficiently similar oridentical content characteristic metadata, e.g. specifying content withsufficiently similar characters, occurring at sufficiently similar oridentical factual or fictional times, or in sufficiently similar oridentical factual or fictional times, as the characteristic metadata ofthe content which has already been utilised (consumed) by the user canbe surfaced by the recommender engine component 238.

In general terms, the recommender engine component 238 is configured toaccess the memory 301 of the backend device 230 and surface one or moreidentifications of individual content items and/or linked transmediacontent subsets to a given user of the system. This is achieved with theidentifications of such content being passed to experience control layer232, as can be seen in FIG. 3 and FIG. 7. In addition, the recommenderengine component 238 can be configured to surface users relevant to thegiven user with one or more identifications of other recommended usersbeing passed to the experience control layer 232. The experience controllayer 232 obtains the received identifications and passes them tofrontend device 210 for display in the user interface 218 via the visualnavigation component 214.

The surfaced content items or linked transmedia content subsets areselected by the recommender engine component 238 based on data about theindividual transmedia content data items and transmedia content subsetsalready stored in the memory 301. In the present context, “surface”means that the selected one or more items of content or users areisolated from other items of content or users and provided as one ormore identifications to the given user, e.g. as a notification on theuser device, or as a special flag associated with the surfaced item.This way, the given user can readily access the content and/orinformation and content associated with other users.

The recommender engine component 238 includes a preference model 242,the function of which is to provide a score of a given transmediacontent data item or transmedia content subset for a specific user ofthe system 200. The preference model 242 is depicted in FIG. 8.

Referring to FIG. 8, the preference model 242 takes as input, data aboutone or more transmedia content data items or transmedia content subsetsand provides as output to an optimisation component 248 a score for eachinput item for a given user. The preference model 242 achieves this by,at a first step 601, removing global effects. Some users might, forexample, tend to constantly give lower ratings (user-assigned scores)than others. Removing this bias before processing the input itemsimproves prediction accuracy. In a second step 602, the model collectsthe predictions of n independent state of the art algorithms (such asRank-based SVD). The system then builds an ensemble prediction at step603 by using a Lasso Regression. In the last step 604, the globaleffects are added back to the ensemble prediction to obtain and output afinal suitability score 605 of all input content and users in respect ofthe given user.

The recommender engine component 238 may also include a user-brand matchcomponent 244, which is configured to provide, for the given user, atleast one prediction of a preference for a given branded content dataitem, and a branded content model 246 that provides, for a giventransmedia content data item, a prediction of the suitability of a givenbranded content data item, e.g. an advertisement.

As shown in FIG. 7, the transmedia content recommender engine 238additionally implements an optimisation component 248 which isconfigured to query at least the preference model component 242, andoptionally the user-brand match component 244 and brand model component246 by providing them with a transmedia content parameter identifyingitems of content in use and/or a user identification for the given user.The optimisation component 248 acts on the suitability scores itreceives to maximise content and/or user recommendations. Reference hereto “content in use” generally means the content currently selectedand/or being manipulated by the given user in the user interface 218.

For the case of preference model data only, the optimisation component248 outputs to the control layer 232 all identifications for recommendedcontent (or linked subsets) and/or users which have a determinedsuitability score which sufficiently meets a satisfactory thresholdparticular to the given user or content currently being utilised by thegiven user. By meeting a particular threshold, this may mean that thesuitability score has to be above, or below a particular fixed vale,dependant on whether the score is arranged to be ascending or descendingrespectively in respect of suitability. Alternatively, the optimisationcomponent 248 can rank suitability scores (either for all content or allcontent which sufficiently meets a threshold) and then output apredetermined number of different content or users identifications downfrom the top-ranked scores. For example, the optimisation component 248may output identifications for transmedia content items (or linkedcontent subsets) which been determined in respect of the given user tohave the top X highest suitability scores (or lowest depending onwhether scores are ascending or descending), as determined by thepreference model 242, where X may be 5, 10, 20, 30, 40, 50 or 100. Thisone-dimensional optimisation ensures that the given user is engaged byrelevant content, particularly in respect of linked content subsets.

In a further embodiment, a three-dimensional optimisation canadditionally take into account user-brand matching and brand modelling.In this embodiment, the optimisation component 248 maximises the sum ofthe output for the preference model component 242, user-brand matchcomponent 244 and brand model component 246 over the transmedia contentparameter. This three-dimensional optimisation can ensure that users areboth engaged by relevant content, and the content being consumed contentwhich is pertinent a brand relevant to the particular user or existingutilised content.

The recommender engine component 238 may also take into account a givenuser's defined preferences, historical behaviour and other predictedproperties such as future user behaviour, or inferred properties such asan emotional state. In order to achieve this, the recommender enginecomponent 238 communicates with the user model component 234 depicted inFIG. 3 and shown in more detail in FIG. 9.

FIG. 9 depicts data flows between some components of the backend device230 in the context of the user model component 234, in particular, theinput and output components of the user model component 234, many ofwhich are also the input and output components of the recommender enginecomponent 238. Referring to FIG. 9, the user model component 234, 234includes a state model 701, behaviour model 702, user profile 703,interaction model 704 and talent harvest component 705.

Modelling the state of the user, using state model component 701 permitspersonalised recommendations to be provided by the recommender enginecomponent 238, and also provides accurate predictions of user behaviourby the behaviour model component 702. The state model component 701 mayalso be used to customise the user interface 218 and encourage users tocreate new content, i.e. new transmedia content data items andtime-ordered content links, within the system 200. The state modelcomponent 701 represents and predicts the continuous creational,emotional and affective state of users of the system 200. The creationalstate describes if the user is in the mood for consuming transmediacontent subsets or non-linear networks or contributing their owntransmedia content data items and time-ordered content links. Theaffective state indicates whether the user is, for example, motivated orbored. The emotional state describes the emotions that individualtransmedia content data items or transmedia content subsets/non-linearnetworks trigger in the user.

Due to the hierarchy of the system, i.e. the logical separation oftransmedia content into levels of the individual transmedia content dataitems, transmedia content subsets, non-linear networks and storyuniverse, user behaviour is predicted at different levels of granularityin two main dimensions, namely: (1) the transmedia content hierarchy;and (2) the interaction of users with other users. User dynamics are ofinterest at different levels of user granularity, for example in respectof: single users, small groups, audiences. The behaviour model component702 predicts the user behaviour in both of these dimensions and providesinsights into dynamic behaviour for load balancing, information aboutthe most likely path of a user through a given non-linear network andpredicts the evolution of the whole system 200.

The user preference component 703 provides a detailed profiling for eachuser of the system 200, including, for example, demographics (e.g. age,gender, etc.), personality traits (e.g. Big 5) and location data (e.g.GPS).

The interaction model component 704 monitors and predicts socialinteractions of users. The analysis and prediction of socialinteractions of groups of contributors can be used by the system 200 toencourage collaboration across all levels of the system, predict thequality of stories and influence user community building.

Talent harvest component 705 identifies users of the system 200 withexceptional talent at creating transmedia content, and categorises theseusers according to their type of talent, e.g. a Star Wars expert, anartist doing funny comic strips or a user resonating with a specificaudience.

As shown in FIG. 9, the user model component 234 is in communicationwith the story model component 236, the recommender engine component 238and the metadata extraction component 240, with these components beingboth inputs and outputs to the user model component 234, allowing dataexchange therebetween.

The state model component 701 within the user model predicts or infersthe state of a given user from (noisy) observations of the userinteracting with the system 200. The state model component 701 uses astatistical model, such as a hidden Markov model to infer a creationalstate of the user (i.e. on a scale from creative to consumptive; seeFIG. 1), the user's emotional state (e.g. sad, happy, excited, etc.) andthe affective state of the user (e.g. engaged with the system 200,bored, etc.). The state model component 701 further classifies thesecontinuous states into a set of discrete states for further processing(or similar). The observed variables include the profile of the user(e.g. personality), the history of interactions of the user with thesystem, properties of stories/story elements the user is actuallyconsuming or creating, the environment of the user (e.g. office) andtemporal metrics (e.g. working day versus weekends).

The state model component 701 represents user behaviour across differentscales/levels of the system (i.e. individual transmedia content dataitems, transmedia content sub-sets or non-linear networks) by creating ahierarchical, graph-based user model that refines modelled action andstates with every level. User behaviour is modelled across differentlevels of granularity: crowd (e.g. predict behaviour of large numbers ofusers), community (e.g. defined, sorted groups of users), and at anindividual level.

In one embodiment of the invention, the user state and actions arerepresented by a Hidden Markov Model 900, depicted in FIG. 11. In thiscase, the latent variable 901 represents the creational state of theuser. The observed variable 902 represents the actions of the user, suchas “consuming a transmedia content data item”, “rating a transmediacontent subset”, “creating a new transmedia content data item”, etc. Thecreational state changes dynamically over time and can be inferred fromthese observed user actions. As a Dynamic Bayesian Network (DBN), thisrepresentation can be extended to include the engagement and emotionalstate of the user as well as the interactions between users.

Navigating through a large quantity of transmedia content data items,transmedia content subsets and non-linear networks that are provided tousers by the system 200, in a way that users and user groups can createand consume the data quickly on a range of devices, including personalcomputers, laptops, tablets, mobile devices etc. is challenging. Theuser interface component 218 guides the user in a non-disruptive way,whilst also avoiding repetitive meandering and visual overload ofcontent creation and navigation tools on the multiple, hierarchicallevels of the transmedia content.

The user interface component 218 presents the transmedia content dataitems, transmedia content subsets and non-linear networks as follows. Athree-dimensional representation can be utilised based on one or morethree-dimensional shapes which can be manipulated by the user. In atwo-dimensional system involving a two-dimensional display screen, atwo-dimensional representation of the three-dimensional shape(s) is/aregenerated, and the shape(s) utilised may be one or more spheres orellipsoids, which use hierarchically-connected visual metaphors toprovide further information on the transmedia content data items and howthey are related and connected in a time-based manner for users andgroups of users. This is achieved in a non-disruptive andnon-distracting manner. It will be appreciated that anythree-dimensional object may be used to present the transmedia content.In one embodiment of the present invention, the visual metaphors canequate to features of a planet, such as continental landmasses, oceans,clouds, mountain ranges and coastlines.

FIGS. 10A and 10B depict an example of the user interface that ispresented to a user of the system 200 at different levels of thehierarchical tree structure. FIG. 10A depicts a higher-level view of thetransmedia content 800 which depicts several spheres 801, 802. Eachsphere 801, 802 represents a storyworld, i.e. groups of transmediacontent subsets and non-linear networks that are semantically similar,e.g. the constituent transmedia content data items relate to the samestory characters or story universe. The spheres themselves may bevisually clustered together in the displayed three-dimensionalrepresentation according to semantic similarity between the storyworlds.

A user may select one of the spheres 801, 802, which causes the userinterface 800 to transition to a modified user interface 810, whichdepicts the selected single sphere 811 with additional detail.Additional surface features of the selected sphere 811 are displayed inuser interface 810, such as individual transmedia content subsets ornon-linear networks indicated by icons 813, and representative images ofthe content 814. The visual metaphors are provided such thatsemantically similar transmedia content subsets and non-linear networksare depicted on the same continents 812 on the surface of the planet811. When a user wishes to consume or edit an individual transmediacontent subset or non-linear network, the user can select one of theicons 813 or images 814 and the user interface 810 transitions to show agraph-structure of the subset/network and/or the individual transmediacontent data items.

FIG. 12 depicts a method for surfacing transmedia content to a givenuser of the system 200 from the memory 301 of the server device 230. Atstep 1201, the server device 230 receives, e.g. via the networkinterface, user input indicative of a time-ordering between at least twouser-selected transmedia content items. At step 1202, the processingcircuitry 302 of the server device 230 generates, using a transmediacontent linking engine, the content linking data for storage in thememory 301 thereby defining a linked transmedia content subset includingthe at least two user-selected transmedia content items. At step 1203,the processing circuitry 302 accesses the memory 301 and surfacestransmedia content items, linked transmedia content, identifications ofidentified users other than the given user and/or the content itemsassociated with at least one identified user other than the given user.The surfaced content items and/or the surfaced linked transmedia contentsubsets are surfaced for selection by the given user via the transmediacontent linking engine as one or more user-selectable transmedia contentitems.

While some exemplary embodiments of the present disclosure have beenshown in the drawings and described herein, it will be appreciated thatthe methods described herein may be deployed in part or in whole througha computing apparatus that executes computer software, program codes,and/or instructions on processing circuitry, which may be implemented byor on one or more discrete processors. As a result, the claimedelectronic device, apparatus and system can be implemented via computersoftware being executed by the processing circuitry. The presentdisclosure may be implemented as a method in a system, or on anapparatus or electronic device, as part of or in relation to theapparatus or device, or as a computer program product embodied in acomputer readable medium executable on one or more apparatuses orelectronic devices.

A processor as disclosed herein may be any kind of computational orprocessing device capable of executing program instructions, codes,binary instructions and the like. The processor may be or may include asignal processor, digital processor, embedded processor, microprocessoror any variant such as a co-processor (math co-processor, graphicco-processor, communication co-processor and the like) and the like thatmay directly or indirectly facilitate execution of program code orprogram instructions stored thereon. Each processor may be realized asone or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. A givenprocessor may also, or instead, be embodied as an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. In addition, each processor may enableexecution of multiple programs, threads, and codes. The threads may beexecuted simultaneously to enhance the performance of the processor andto facilitate simultaneous operations of the application. By way ofimplementation, the methods, program codes, program instructions and thelike described herein may be implemented in one or more thread. Thethread may spawn other threads that may have assigned prioritiesassociated with them; the processor may execute these threads based onpriority or any other order based on instructions provided in theprogram code.

Each processor may access one or more memories, for example one or morenon-transitory storage media which store the software, executable code,and instructions as described and claimed herein. A storage mediumassociated with the processor for storing methods, programs, codes,program instructions or other type of instructions capable of beingexecuted by the computing or processing device may include but may notbe limited to one or more of a CD-ROM, DVD, memory, hard disk, flashdrive, RAM, ROM, cache and the like.

The methods and/or processes disclosed herein, and steps associatedtherewith, may be realized in hardware, software or a combination ofhardware and software suitable for a particular application. Thehardware may include a general purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, the methods described above and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in a system that performs the stepsthereof, and may be distributed across electronic devices in a number ofways, or all of the functionality may be integrated into a dedicated,standalone electronic device or other hardware. In another aspect, themeans for performing the steps associated with the processes describedabove may include any of the hardware and/or software described above.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising”, “having”, “including”, and “containing”are to be construed as open-ended terms (i.e. meaning “including, butnot limited to”) unless otherwise noted. Recitation of ranges of valuesherein are merely intended to serve as a shorthand method of referringindividually to each separate value falling within the range, unlessotherwise indicated herein, and each separate value is incorporated intothe specification as if it were individually recited herein.

The use of any and all examples, or exemplary language (e.g. “such as”)provided herein, is intended merely to better illuminate the disclosureand does not pose a limitation on the scope of the disclosure unlessotherwise claimed.

The present disclosure has been provided above by way of example only,and it will be appreciated that modifications of detail can be madewithin the scope of the claims which define aspects of the invention.

The invention claimed is:
 1. An apparatus for surfacing transmediacontent to a given user of a plurality of users comprising: a memoryconfigured to store: a plurality of transmedia content data items, eachcontent data item being associated with at least one of the plurality ofusers; and linking data which define time-ordered content links betweenthe plurality of transmedia content data items, the plurality oftransmedia content data items being arranged into linked transmediacontent subsets comprising different groups of the transmedia contentdata items and different content links therebetween, wherein eachtime-ordered content link defines a directional link from a firsttransmedia content data item to a second transmedia content data item ofthe plurality of transmedia content items; a transmedia content linkingengine performed by a processor configured to receive user inputindicative of a time-ordering between at least two user-selectedtransmedia content data items and generate the linking data for storagein the memory, thereby defining a linked transmedia content subsetincluding the at least two user-selected transmedia content data items,wherein the transmedia content linking engine is configured to identifya current user-selected time-ordered location of the given user within agiven linked transmedia content subset for insertion of one of thetransmedia content data items, and to generate corresponding contentlinking data therefor upon insertion by the given user of the one ormore content data items into the given linked transmedia content subset;and a recommender engine component, performed by the processor,configured to access the memory and surface to the given user at leastone of the following based on the current user-selected time-orderedlocation: one or more content items of the plurality of transmediacontent data items, one or more of the linked transmedia content subsetsof the linked transmedia content subsets, one or more identifications ofidentified users other than the given user, and the content items of theplurality of transmedia content data items associated with at least oneidentified user other than the given user, wherein the one or moresurfaced content data items and/or the one or more surfaced linkedtransmedia content subsets are surfaced for selection by the given uservia the transmedia content linking engine as one or more user-selectabletransmedia content data items.
 2. The apparatus of claim 1, wherein thememory further includes user ownership data which associates eachtransmedia content data item to a user of the plurality of users, andeach transmedia content subset of multiple content subsets with a userof the plurality of users.
 3. The apparatus of claim 1, furthercomprising a preference model, wherein the preference model isconfigured to provide a prediction of a rating for a given user for agiven transmedia content data item.
 4. The apparatus of claim 3, whereinthe preference model is configured to: remove global effects for one ormore rated items that have been previously rated by the given user togenerate explicit ratings and storing the removed global effects;provide the explicit ratings and metadata associated with the giventransmedia content data item to a plurality of recommender algorithms togenerate a plurality of predicted ratings for the given transmediacontent data item; combine the plurality of predicted ratings to buildan ensemble prediction of the predicted ratings for the given transmediacontent data item; add the stored global effects to the ensembleprediction to produce a predicted user rating for the given transmediacontent data item; and output the predicted user rating for the giventransmedia content data item.
 5. The apparatus of claim 3, wherein theapparatus further comprises a user-brand match component configured toprovide for a given user a prediction of a preference for a givenbranded content data item.
 6. The apparatus of claim 5, wherein theapparatus further comprises a branded content model configured toprovide for a given transmedia content data item a prediction of asuitability of a given branded content data item.
 7. The apparatus ofclaim 6, wherein the recommender engine component is configured tosurface transmedia content data items by querying the preference model,user-brand match component and brand model component by providing thepreference model, user-brand match component and brand model with atransmedia content parameter, user data for the given user and a givenbranded content data item, and to maximize a sum of an output for thepreference model, user-brand match component and brand model over thetransmedia content parameter.
 8. The apparatus of claim 7, wherein therecommender engine component is configured to surface a transmediacontent data item with a maximum output.
 9. The apparatus of claim 1,wherein, the plurality of transmedia content data items comprises itemsof different content types.
 10. The apparatus of claim 1, wherein thetransmedia content data items relate to narrative elements of thetransmedia content data items.
 11. The apparatus of claim 10, whereinthe time-ordered content links define a narrative order of thetransmedia content data items.
 12. The apparatus of claim 1, wherein thefirst transmedia content data item has a plurality of outgoingtime-ordered content links.
 13. The apparatus of claim 1, wherein thesecond transmedia content data item has a plurality of incomingtime-ordered content links.
 14. The apparatus of claim 1, wherein thememory is further configured to store a plurality of subset entry pointsfor the linked transmedia content subsets.
 15. The apparatus of claim14, wherein each subset entry point is a flag indicating a transmediacontent data item that has at least one outgoing time-ordered link andno incoming time-ordered links.
 16. The apparatus of claim 15, whereineach linked transmedia content subset defines a linear path, wherein alinear path comprises a subset entry point, one or more transmediacontent data items and one or more time ordered links between the subsetentry point and the transmedia content data items.
 17. The apparatus ofclaim 16, wherein two or more transmedia content subsets share one ormore subset entry points, one or more transmedia content data itemsand/or one or more time ordered content links.
 18. The apparatus ofclaim 17, wherein the recommender engine component is further configuredto surface one or more groups transmedia content subsets to the givenuser, the one or more surfaced groups being surfaced for selection bythe given user via the transmedia content linking engine.
 19. Theapparatus of claim 1, further comprising a user model, wherein the usermodel is configured to provide a predictions of a mental state and/or abehavior of the given user to the recommender engine.
 20. The apparatusof claim 19, wherein the memory is further configured to store aplurality of historical user interaction data items which define thehistorical interaction of the given user with the apparatus forsurfacing transmedia content to the given user of a plurality of users,and wherein the user model is configured to: receive one or morehistorical user interaction data items associated with the given user;provide the one or more historical interaction data items to astatistical model to produce a prediction of a mental state of the givenuser; retrieve from the statistical model the predicted mental state ofthe given user and/or the predicted behavior of the given user; andoutput the predicted mental state of the given user and/or the predictedbehavior of the given user.
 21. The apparatus of claim 20, wherein thestatistical model is a hidden Markov model.
 22. The apparatus of claim20, wherein the recommender engine component is further configured tosurface one or more individual content items to the given user and/orsurface one or more of the linked transmedia content subsets to thegiven user when the output of the user model indicates the user is inmental state conducive to consuming transmedia content data items. 23.The apparatus of claim 20, wherein the recommender engine component isfurther configured to surface one or more individual content items tothe given user and/or surface one or more of the linked transmediacontent subsets to the given user when the output of the user modelindicates the predicted behavior of the given user is to consumetransmedia content data items.
 24. A system for surfacing transmediacontent to a given user of a plurality of users comprising: theapparatus of claim 1; an electronic device configured to be incommunication with the apparatus and receive and display to the givenuser: the surfaced one or more content items of the plurality oftransmedia content items; and/or the surfaced one or more linkedtransmedia content subsets of the linked transmedia content subsets;and/or the surfaced one or more identifications of identified usersother than the given user; and/or the surfaced content items of theplurality of transmedia content items associated with at least oneidentified user other than the given user.
 25. A method for surfacingtransmedia content to a given user of a plurality of users from a memoryconfigured to store a plurality of transmedia content data items, eachcontent item being associated with at least one of the plurality ofusers, and linking data which define time-ordered content links betweenthe plurality of transmedia content items, the plurality of transmediacontent items being arranged into linked transmedia content subsetscomprising different groups of the transmedia content items anddifferent content links therebetween, wherein each time-ordered contentlink defines a directional link from a first transmedia content dataitem to a second transmedia content data item of the plurality oftransmedia content items, the method comprising: receiving, at aprocessor, user input indicative of a time-ordering between at least twouser-selected transmedia content items, wherein the processor isconfigured to identify a current user-selected time-ordered location ofthe given user within a given linked transmedia content subset forinsertion of one of the transmedia content data items, and to generatecorresponding content linking data therefor upon insertion by the givenuser of the one or more content data items into the given linkedtransmedia content subset; generating with the processor the contentlinking data for storage in the memory, thereby defining a linkedtransmedia content subset including the at least two user-selectedtransmedia content items; and accessing the memory and surfacing to thegiven user with a recommender engine component, performed by theprocessor at least one of the following based on the currentuser-selected time-ordered location: one or more transmedia contentitems of the plurality of transmedia content data items, one or more ofthe linked transmedia content subsets of the linked transmedia contentsubsets, one or more identifications of identified users other than thegiven user, and the transmedia content items of the plurality oftransmedia content data items associated with at least one identifieduser other than the given user, wherein the one or more surfaced contentdata items and/or the one or more surfaced linked transmedia contentsubsets are surfaced for selection by the given user via the transmediacontent linking engine as one or more user-selectable transmedia contentdata items.
 26. A non-transitory computer readable medium comprisingcomputer executable instructions, which when executed by a computer,cause the computer to perform the steps of the method of claim 25.